ALTERNATIVE ASYMPTOTICS AND THE PARTIALLY LINEAR MODEL WITH MANY REGRESSORS
Name
Cattaneo-Jansson-NeweyAlternativeAsymptotics-July2
Size
262.3 KB
Format
Unknown
Checksum (MD5)
6eddbdf09c2b97f8c0c05b490842464b
Author(s)
Cattaneo, Matias D.
Jansson, Michael
Newey, Whitney K
Date Issued
January 2016
Journal
Econometric Theory
Publisher
Cambridge University Press
Citation
Cattaneo, Matias D. et al. “ALTERNATIVE ASYMPTOTICS AND THE PARTIALLY LINEAR MODEL WITH MANY REGRESSORS.” Econometric Theory (October 2016): 1–25 © 2016 Cambridge University Press
Version
Original manuscript
Abstract
Many empirical studies estimate the structural effect of some variable on an outcome of interest while allowing for many covariates. We present inference methods that account for many covariates. The methods are based on asymptotics where the number of covariates grows as fast as the sample size. We find a limiting normal distribution with variance that is larger than the standard one. We also find that with homoskedasticity this larger variance can be accounted for by using degrees-of-freedom-adjusted standard errors. We link this asymptotic theory to previous results for many instruments and for small bandwidth(s) distributional approximations. Keywords: non-standard asymptotics; partially linear model; many terms; adjusted variance
MIT Department
Massachusetts Institute of Technology. Department of Economics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1017/S026646661600013X
